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12 articles · Page 1 of 1
Lisa Notes
Lisa Notes
Jun 30, 2026 · Artificial Intelligence

NLP Study Notes: 4 Essential Steps for Preprocessing Chinese Text Corpora

This article walks through the four core steps of Chinese NLP corpus preparation—collecting data, cleaning it with regex and encoding detection, tokenizing using dictionary‑based or statistical methods such as jieba, HMM and CRF, and finally standardizing with stop‑word removal, vocabulary building and one‑hot encoding—while illustrating each step with concrete code snippets and practical examples.

CRFChineseNLP
0 likes · 12 min read
NLP Study Notes: 4 Essential Steps for Preprocessing Chinese Text Corpora
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 21, 2023 · Artificial Intelligence

Problems and Solutions in Semantic Segmentation: An Overview of DeepLabV1

This article explains the two main challenges of applying deep convolutional neural networks to semantic segmentation—signal down‑sampling and loss of spatial precision—and describes how the DeepLabV1 architecture, using dilated convolutions, large‑field‑of‑view modules, fully‑connected CRF and multi‑scale fusion, addresses these issues while achieving faster, more accurate segmentation results.

CRFDeepLabV1Semantic Segmentation
0 likes · 12 min read
Problems and Solutions in Semantic Segmentation: An Overview of DeepLabV1
58 Tech
58 Tech
Aug 19, 2021 · Artificial Intelligence

Practical NER Techniques for Business Chatbots on the 58.com Service Platform

This article presents a comprehensive case study of applying named‑entity‑recognition (NER) techniques to the smart chat assistant of 58.com’s yellow‑page service, covering business background, model selection (BiLSTM‑CRF, IDCNN‑CRF, BERT), data‑augmentation, focal loss, fusion of rule‑based and neural methods, context modeling, online performance, and future research directions.

BERTCRFData Augmentation
0 likes · 16 min read
Practical NER Techniques for Business Chatbots on the 58.com Service Platform
58 Tech
58 Tech
May 24, 2021 · Artificial Intelligence

Tag Extraction for 58 Yellow Pages Posts Using Sequence Labeling and Model Optimization

This article describes a complete solution for extracting and normalizing tags from 58 Yellow Pages service posts, covering candidate word acquisition, sequence‑labeling models such as CRF and BERT‑CRF, hierarchical softmax optimization for massive label spaces, and experimental results on both post content and user reviews.

BERTCRFhierarchical softmax
0 likes · 20 min read
Tag Extraction for 58 Yellow Pages Posts Using Sequence Labeling and Model Optimization
58 Tech
58 Tech
Jan 29, 2021 · Artificial Intelligence

Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

This article details the background, challenges, architecture, model selection, and future directions of the business‑opportunity slot recognition module used in 58.com’s intelligent customer service, highlighting how regex‑model fusion and IDCNN‑CRF improve entity extraction for phone, WeChat, address, and time slots.

BERTCRFIDCNN
0 likes · 11 min read
Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service
DataFunSummit
DataFunSummit
Dec 27, 2020 · Artificial Intelligence

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

This article provides a comprehensive overview of sequence labeling tasks in NLP, covering their definition, common tag schemes (BIO, BIEO, BIESO), comparisons with other NLP tasks, major modeling approaches such as HMM, CRF, RNN and BERT, real‑world applications like POS tagging, NER, event extraction and gene analysis, and a step‑by‑step PyTorch implementation with dataset preparation, training pipeline, and evaluation metrics.

BERTCRFHMM
0 likes · 27 min read
Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation
JD Tech Talk
JD Tech Talk
Dec 3, 2020 · Artificial Intelligence

Consumer Behavior Cause Extraction with BERT Fine‑tuning and a Novel Sequence‑Labeling Framework (ICDM 2020 Winning Solution)

At ICDM 2020, the JD Digits Silicon Valley team achieved top results in the Knowledge Graph Contest by fine‑tuning BERT and introducing a novel sequence‑labeling framework that jointly extracts consumer behavior types and their underlying reasons, leveraging CRF decoding and model ensemble for superior performance.

BERTCRFICDM 2020
0 likes · 11 min read
Consumer Behavior Cause Extraction with BERT Fine‑tuning and a Novel Sequence‑Labeling Framework (ICDM 2020 Winning Solution)
HomeTech
HomeTech
Nov 13, 2019 · Artificial Intelligence

Sequence Labeling for Entity Recognition in Automotive Search: Techniques and Applications

This article examines how sequence labeling methods such as pattern matching, CRF, and deep‑learning models like BiLSTM‑CRF and BERT are applied to automotive search tasks—including car‑series, model, and location/entity recognition—detailing their development, implementation challenges, and performance results.

AutomotiveBERTCRF
0 likes · 11 min read
Sequence Labeling for Entity Recognition in Automotive Search: Techniques and Applications
58 Tech
58 Tech
May 31, 2019 · Artificial Intelligence

Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT

This article reviews modern deep‑learning methods for Chinese word segmentation, comparing traditional CRF‑based approaches with BiLSTM‑CRF and BERT models, describing their architectures, training procedures, experimental results, and practical considerations for deployment.

BERTBiLSTMCRF
0 likes · 17 min read
Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT
Sohu Tech Products
Sohu Tech Products
Apr 11, 2019 · Artificial Intelligence

Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation

This article reviews the challenges of media‑domain named entity recognition, outlines the evolution from rule‑based methods through traditional machine‑learning and deep‑learning models to attention‑based Transformers, and details Sohu’s practical Bi‑LSTM‑CRF system with data‑annotation strategies and performance results.

Bi-LSTMCRFNER
0 likes · 12 min read
Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation
58 Tech
58 Tech
Jan 22, 2019 · Artificial Intelligence

Chinese Word Segmentation: Challenges, Methods, and Practical Practices

The article explains why Chinese word segmentation is essential for NLP tasks, outlines its fundamental difficulties such as ambiguity and out‑of‑vocabulary words, reviews dictionary‑based, statistical, and CRF approaches, and shares practical experiences from 58 Search’s production system.

CRFLanguage ModelNLP
0 likes · 21 min read
Chinese Word Segmentation: Challenges, Methods, and Practical Practices